Methods and apparatus for identification of polymeric species from mass spectrometry output

ABSTRACT

Methods and apparatus are provided for the identification of one or more candidate chemical formulas from mass spectrometry data corresponding to an unidentified chemical compound. By restricting the generation of candidate formulas to those having repeating units and/or end units with specified limitations, the methods and apparatus may more efficiently iteratively search for a chemical formula having matching mass spectrometry output within a threshold tolerance. In another aspect, methods and apparatus are provided for the identification of one or more candidate chemical formulas from mass spectrometry data based at least in part upon neutral loss.

RELATED APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 13/751,871 (now U.S. Pat. No. 9,410,926), entitled “Methods andApparatus for Identification of Polymeric Species from Mass SpectrometryOutput” and filed on Jan. 28, 2013. U.S. Pat. No. 9,410,926 claimspriority to and the benefit of U.S. Provisional Patent Application No.61/681,575 entitled “Methods and Apparatus for Identification ofPolymeric Species from Mass Spectrometry Output” and filed Aug. 9, 2012,and U.S. Provisional Patent Application No. 61/696,071 entitled “Methodsand Apparatus for Identification of Polymeric Species from MassSpectrometry Output” and filed Aug. 31, 2012, the contents of each ofwhich is hereby incorporated by reference in its entirety.

BACKGROUND

Mass Spectrometry (MS) is an analytical tool that measures themass-to-charge ratio of charged particles, and is widely used for thequalitative and quantitative analysis of chemical compounds includingcompound identification, as well of interrogation of compound'sstructure, selective reactivity, stability, etc. Commercially availablemodern mass spectrometers not only employ different methods ofseparation of ions, but also vary in evaporation/ionization techniques,as well as detection schemes. This results in an ever-broadening rangeof scientific applications based on or related to mass spectrometricmeasurements.

The first generation of commercial mass spectrometers for analyticalchemistry used an electron impact ionization technique, which, in itsoptimal mode of 70 eV electron energy, routinely overexcited analytemolecules, resulting in a rapid gas phase unimolecular decomposition ofsignificant portion of parent ions. This produced a characteristicanalyte “signature” spectrum—a mixture of peaks of parent ion and itsfragments. These spectra were quickly recorded and organized into theso-called MS-libraries, used even today as an identification tool formass spectrometry. However, electron impact, a gas-phase ionizationtechnique, relies on up-front evaporation of a sample, easily realizedonly for volatile low-to-medium mass range analyte molecules. Foranalytes above 300 Da not only evaporation is problematic, but alsoelectron-impact induced fragmentation becomes too complex.

Discovery of modern “soft” ionization techniques which produce “cold”analyte molecular ions, starting with chemical ionization, solved theproblem of post-ionization dissociation and complexity of the immediatespectra, but added to the ambiguity of the parent ion identityassignment. Without the background dissociation, the “signature” featureof an analyte molecular ion was gone. But the only real advance ofchemical ionization was to eliminate the post-ionization dissociation.Since it was the first of the soft techniques, scientists had roughly adecade to find ways to re-introduce dissociation into the massspectrometry—hoping to regain the “signature” feature as a tool to solvethe problem of analyte identification. It came in the form ofcommercially-developed tandem mass spectrometers: devices in whichcreation of the analyte molecular ion is separated (in space or time)from the event of its fragmentation.

Chemical (charge transfer) ionization saw about a decade of itsrenaissance between 1975 and 1985, before the advantages of newionization techniques opened up a new era in analytical massspectrometry. Fast atom bombardment (FAB), electrospray ionization (ESI)and matrix assisted laser desorption ionization (MALDI) have coupled twoprincipal steps involved in mass spectrometry analysis: evaporation andionization, thus allowing for mass-spectral analysis of large molecules.It took the scientific community about a decade to catch up with thetechnology, culminating in the current explosion of massspectrometry-based applications in analytical synthetic chemistry,pharmacology, ecology, biology, food science, etc.

Regardless of a compound's physical or chemical properties that are ofultimate scientific interest, the first goal of mass spectrometry is toestablish the compound's identity. At the most basic level ofidentification, the molecular formula (elemental composition) of the ionneeds to be determined. Higher order information (molecular structure,conformation, stability, etc.) can be revealed through gas-phasechemistry in multi-stage mass spectrometry experiments, and/or incombination with “orthogonal” techniques, such as chromatography,electrophoresis, ion mobility, spectroscopy, etc. For relatively smallmolecules, knowledge of exact mass and relative abundance of isotopesmay be sufficient to reveal molecular formula information, even in theabsence of the background fragmentation. In any case, potentialcandidate molecular formulas need to be either referenced frompreviously established lists (databases) or generated by nested loopsummations, realizing possible combinations of number of atoms ofdifferent types (carbon, oxygen, hydrogen, etc.) in attempt to matchexperimentally observed masses with required precision. Historically,the latter approach of generating formulas as atomic combinations wasthe only available approach. While the former approach of the so-called“known unknown” target analysis has gained popularity as databases ofknown compounds are publicly available and continue to grow, even todayin special applications such as large polymer synthesis, informationoffered by public databases may not be sufficient to provideidentification base for researcher's needs. In such cases, a massspectrometry specialist still needs to revisit the old formulageneration approach in an attempt to assign molecular formulas toexperimentally observed mass spectrometric peaks. Unfortunately, currentformula generator algorithms are based on nested loops and areinherently susceptible to exponential dependence of computational coston (a) the number of atoms types assumed to be comprising the potentialformula, and (b) of the mass of the target ion.

Algorithmic improvements to the molecular formula generation modelsremain very relevant even today, as a potential way to improve the firststep of mass spectrometric investigations: identification of the atomiccomposition of ionic species.

SUMMARY

A software tool is provided for the identification of one or morecandidate chemical formulas from mass spectrometry data corresponding toan unidentified chemical compound. By restricting the generation ofcandidate formulas to those having repeating units and/or end units withspecified limitations, the software tool may more efficientlyiteratively search for a chemical formula having matching massspectrometry output within a threshold tolerance. In another aspect, asoftware tool is provided for the identification of one or morecandidate chemical formulas from mass spectrometry data based at leastin part upon neutral loss.

To aid in the speed and accuracy of identification of an unknownpolymeric chemical compound from mass spectrometry data, where theunknown compound includes a repeating unit and one or more end units, asoftware tool for chemical formula identification is provided whichidentifies one or more candidate structures given the mass spectrometrydata and additional data including: (i) identification of one or morecandidate repeating units; (ii) identification of a set of chemicalelements in the one or more end units; and/or (iii) identification of amaximum number of chemical elements in the one or more end units (e.g.,maximum of each chemical element or maximum total chemical elements). Insome implementations, the software tool provides a graphical userinterface for performing the above identification. In someimplementations, the software tool is used to identify an unknownpolymeric species having a repeating unit that repeats at least threetimes. By restricting the generation of candidate formulas to thosehaving repeating units and/or end units with specified limitations, thesoftware tool may more efficiently iteratively search for a chemicalformula including the repeating units plus end unit structures matchingthe mass spectrometry output within a threshold tolerance.

In some implementations, the software tool accepts both a repeating unitstructure (or its exact mass) and a limitation upon the structure orcomposition of one or both end unit(s). For example, a user may limitthe number of elements composing one or both end unit(s) to a maximumnumber of elements (e.g., 10). In another example, a user may limit thetype of elements (e.g., the element species) to a particular set.

In another implementation, a software tool for chemical formulaidentification is provided for the identification of a chemical formulafrom mass spectrometry data based at least in part upon neutral loss, amass difference between two mass spectral peaks due to processing suchas front-end chemistry or gas-phase fragmentation which results in aloss of molecular formula. This software tool may increase accuracy ornarrow a pool of potential candidates of chemical formulas in relationto the following examples: collision-induced disassociation incapillary-skimmer region of a TOF mass spectrometer: metabolites inbulk; front-end chemical reactions (e.g., reactions in a sample prior tomass spectrometry); and polymer identity in relation to exact mass ofthe repeating unit. In some implementations, the software tool providesa graphical user interface for performing the above identification.

In another implementation, a software tool for chemical formulaidentification is provided for the identification of a polymericcompound from mass spectrometry data by restricting the generation ofcandidate formulas to those having repeating units and/or end units withspecified limitations and by identifying and using a measure of neutralloss to narrow a pool of potential candidates of chemical formulas.

In one aspect, the invention is directed to a method for identifying aspecies of an unidentified chemical compound including two or morerepeating structural units, the method including accessing at least aportion of mass spectrometry data, where the portion of massspectrometry data relates to a sample including the unidentifiedchemical compound, where the unidentified chemical compound includes (a)the two or more repeating structural units, and (b) at least one endunit. The method may include determining at least one of (a) a chemicalformula of the repeating structural unit, and (b) an estimated weight ofthe two or more repeating structural units, and identifying, by aprocessor of a computing device, one or more candidate chemical formulasfor the unidentified chemical compound based at least in part upon themass spectrometry data, and based further in part on at least one of (a)the chemical formula of the repeating structural unit, and (b) theestimated weight.

In certain embodiments, each repeating structural unit of the two ormore repeating structural units has a same chemical formula, and the atleast one end unit has a chemical formula different than the chemicalformula of the two or more repeating structural units.

In certain embodiments, the method includes determining a set ofcandidate chemical elements, where the chemical formula of any end unitof the at least one end unit consists of one or more elements of the setof candidate chemical elements. The method may include determining amaximum number of each chemical element of the set of candidate chemicalelements, where the chemical formula of any end unit of the at least oneend unit consists of no more than the maximum number of each chemicalelement of the set of candidate chemical elements. Identifying the oneor more candidate chemical formulas for the unidentified chemicalcompound may include identifying the one or more candidate chemicalformulas for unidentified chemical compound based further in part uponthe set of candidate chemical elements.

In certain embodiments, the method includes identifying, from theportion of the mass spectrometry data, an estimated weight of theunidentified chemical compound, where identifying the one or morecandidate chemical formulas for the unidentified chemical compoundincludes identifying the one or more candidate chemical formulas for theunidentified chemical compound based further in part upon the estimatedweight of the unidentified chemical compound. Identifying the one ormore candidate chemical formulas for the unidentified chemical compoundmay include iteratively adding combinations of possible element types toidentify a number of potential element combinations for the one or moreend units, where a calculated weight of each potential elementcombination of the number of potential element combinations, when summedwith the estimated weight of the two or more repeating units, is withina threshold weight of the estimated weight of the unidentified chemicalcompound. The method may further include calculating the estimatedweight of the two or more repeating units. Calculating the estimatedweight may include calculating a weight of a first candidate chemicalformula of the one or more candidate chemical formulas, and multiplyingthe weight of the first candidate chemical formula by a maximumpotential number of repetitions.

In certain embodiments, the method includes, after identifying the oneor more candidate chemical formulas for the unidentified chemicalcompound, for each candidate chemical formula of the one or morecandidate chemical formulas: obtaining mass spectrometry data for therespective candidate chemical formula; and comparing theoreticalspectral data of the mass spectrometry data for the respective candidatechemical formula to experimental spectral data of the portion of massspectrometry data. The method may further include, based in part on atleast one respective result of comparing the theoretical spectral dataof the mass spectrometry data of each candidate chemical formula of theone or more candidate chemical formulas to the theoretical spectral dataof the portion of mass spectrometry data, ranking the one or morecandidate chemical formulas. The method may further include, for atleast one candidate chemical formula of the one or more candidatechemical formulas, based in part upon a respective result of comparingthe theoretical spectral data of the mass spectrometry data of the atleast one candidate chemical formula to the experimental spectral dataof the portion of mass spectrometry data, discarding a first candidatechemical formula of the at least one candidate chemical formula.

In certain embodiments, the method further includes presenting the oneor more candidate chemical formulas to a user within a graphical userinterface.

In one aspect, the invention is directed to a system including aprocessor; and a memory storing instructions thereon, where theinstructions when executed cause the processor to access at least aportion of mass spectrometry data, where the portion of massspectrometry data relates to a sample including an unidentified chemicalcompound, where the unidentified chemical compound includes (a) the twoor more repeating structural units, and (b) at least one end unit. Theinstructions may cause the processor to determine at least one of (a) achemical formula of the repeating structural unit, and (b) an estimatedweight of the two or more repeating structural units. The instructionsmay cause the processor to identify one or more candidate chemicalformulas for the unidentified chemical compound based at least in partupon the mass spectrometry data, and based further in part on at leastone of (a) the chemical formula of the repeating structural unit, and(b) the estimated weight.

In certain embodiments, each repeating structural unit of the two ormore repeating structural units has a same chemical formula, and the atleast one end unit has a chemical formula different than the chemicalformula of the two or more repeating structural units.

In certain embodiments, the instructions further cause the processor todetermine a first candidate chemical formula of the one or morecandidate chemical formulas is a neutral loss match to the unidentifiedchemical compound, the determining of the neutral loss match includingaccessing spectral data for the first candidate chemical formula, and,for each of a number of spectral peaks of the spectral data: calculatinga respective mass difference between a theoretical mass of the firstcandidate chemical formula and a respective experimental masscorresponding to the spectral peak, and comparing the respective massdifference with a mass of each of one or more corresponding neutralmolecular compositions to identify one or more candidate neutralmolecular compositions corresponding to the spectral peak. The spectraldata may include a collision-induced dissociation (CID) mass spectrum.Identifying at least the first candidate chemical formula may includeidentifying a second candidate chemical formula, the instructionsfurther causing the processor to: determine the second candidatechemical formula is a neutral loss match to the unidentified chemicalcompound; and rank the first candidate chemical formula and the secondcandidate chemical formula as matches to the unknown chemical compoundbased in part upon similarity in neutral loss match. Identifying the oneor more candidate neutral molecular compositions may include identifyingthat each candidate neutral molecular composition of the one or morecandidate neutral molecular compositions includes a respective masswithin range of a mass measurement accuracy of the respectiveexperimental mass of the spectral peak.

In certain embodiments, determining the first candidate chemical formulais a neutral loss match to the unidentified chemical compound mayinclude identifying that a stoichiometry of the first candidate chemicalformula allows for at least a first candidate neutral molecularcomposition of the one or more candidate neutral molecular compositions.Identifying that a stoichiometry of the first candidate formula allowsfor the first candidate neutral molecular composition may includedetermining, for the first candidate neutral molecular composition, anumber of atoms of each type in the first candidate chemical formula isgreater than a number of atoms of each corresponding type in thecandidate neutral loss composition.

In one aspect, the invention is directed to a non-transitory computerreadable medium having instructions stored thereon that, when executedby a processor, cause the processor to perform operations includingaccessing at least a portion of mass spectrometry data, where theportion of mass spectrometry data relates to a sample including anunidentified chemical compound, where the unidentified chemical compoundincludes (a) the two or more repeating structural units, and (b) atleast one end unit. The instructions may cause the processor todetermine at least one of (a) a chemical formula of the repeatingstructural unit, and (b) an estimated weight of the two or morerepeating structural units. The instructions may cause the processor toidentify one or more candidate chemical formulas for the unidentifiedchemical compound based at least in part upon the mass spectrometrydata, and based further in part on at least one of (a) the one or morecandidate chemical formulas, and (b) the estimated weight.

In one aspect, the invention is directed to a method for identifying aspecies of an unidentified chemical compound, the method includingaccessing, by a processor of a computing device, mass spectrometry datafor a sample including the unidentified chemical compound, identifying,by the processor, at least a first candidate chemical formula for theunidentified chemical compound based at least in part on the massspectrometry data, accessing, by the processor, spectral data for thefirst candidate chemical formula; and determining, by the processor, thefirst candidate chemical formula is a neutral loss match to theunidentified chemical compound. The determining of the neutral lossmatch may include, for each of a number of spectral peaks of thespectral data: calculating a respective mass difference between atheoretical mass of the first candidate chemical formula and arespective experimental mass corresponding to the spectral peak, andcomparing the respective mass difference with a mass of each of one ormore corresponding neutral molecular compositions to identify one ormore candidate neutral molecular compositions corresponding to thespectral peak.

In certain embodiments, the spectral data includes a collision-induceddissociation (CID) mass spectrum. Identifying at least the firstcandidate chemical formula may include identifying a second candidatechemical formula. The method may further include determining, by theprocessor, the second candidate chemical formula is a neutral loss matchto the unidentified chemical compound; and ranking, by the processor,the first candidate chemical formula and the second candidate chemicalformula as matches to the unknown chemical compound based in part uponsimilarity in neutral loss match.

In certain embodiments, identifying the one or more candidate neutralmolecular compositions includes identifying that each candidate neutralmolecular composition of the one or more candidate neutral molecularcompositions includes a respective mass within range of a massmeasurement accuracy of the respective experimental mass of the spectralpeak.

In certain embodiments, determining the first candidate chemical formulais a neutral loss match to the unidentified chemical compound furtherincludes identifying that a stoichiometry of the first candidatechemical formula allows for at least a first candidate neutral molecularcomposition of the one or more candidate neutral molecular compositions.Identifying that a stoichiometry of the first candidate formula allowsfor the first candidate neutral molecular composition may includedetermining, for the first candidate neutral molecular composition, anumber of atoms of each type in the first candidate chemical formula isgreater than a number of atoms of each corresponding type in thecandidate neutral loss composition.

In certain embodiments, identifying the first candidate chemical formulaincludes: determining at least one of (a) a chemical formula of arepeating structural unit, and (b) an estimated weight of the two ormore repeating structural units, where the unidentified chemicalcompound includes (i) two or more repeating structural units, and (ii)at least one end unit; and identifying the first candidate chemicalformula for the unidentified chemical compound based at least in partupon the mass spectrometry data, and based further in part on at leastone of (a) the chemical formula of the repeating structural unit, and(b) the estimated weight.

In one aspect, the invention is directed to a system including aprocessor; and a memory having instructions stored thereon, where theinstructions, when executed by the processor, cause the processor to:access mass spectrometry data for a sample including the unidentifiedchemical compound; identify at least a first candidate chemical formulafor the unidentified chemical compound based at least in part on themass spectrometry data; access spectral data for the first candidatechemical formula; and determine the first candidate chemical formula isa neutral loss match to the unidentified chemical compound. Thedetermining of the neutral loss match may include, for each of a numberof spectral peaks of the spectral data: calculating a respective massdifference between a theoretical mass of the first candidate chemicalformula and a respective experimental mass corresponding to the spectralpeak, and comparing the respective mass difference with a mass of eachof one or more corresponding neutral molecular compositions to identifyone or more candidate neutral molecular compositions corresponding tothe spectral peak.

In one aspect, the invention is directed to a non-transitory computerreadable medium having instructions stored thereon, where theinstructions, when executed by a processor, cause the processor to:access mass spectrometry data for a sample including the unidentifiedchemical compound; identify at least a first candidate chemical formulafor the unidentified chemical compound based at least in part on themass spectrometry data; access spectral data for the first candidatechemical formula; and determine the first candidate chemical formula isa neutral loss match to the unidentified chemical compound. Thedetermining of the neutral loss match may include, for each of a numberof spectral peaks of the spectral data: calculating a respective massdifference between a theoretical mass of the first candidate chemicalformula and a respective experimental mass corresponding to the spectralpeak, and comparing the respective mass difference with a mass of eachof one or more corresponding neutral molecular compositions to identifyone or more candidate neutral molecular compositions corresponding tothe spectral peak.

Features of the embodiments described with respect to other aspects ofthe invention may be used in this aspect of the invention as well.

BRIEF DESCRIPTION OF THE FIGURES

The foregoing and other objects, aspects, features, and advantages ofthe present disclosure will become more apparent and better understoodby referring to the following description taken in conjunction with theaccompanying drawings, in which:

FIG. 1 is a diagram of an example system for identification of chemicalformulas from mass spectrometry output;

FIGS. 2A through 2C are flow charts of example methods foridentification of polymer species from mass spectrometry output;

FIGS. 3A through 3F are screen shots of an example user interface to asystem for identification of polymer species from mass spectrometryoutput;

FIGS. 4A and 4B are flow charts of an example method for identificationof a chemical formula based at least in part upon neutral loss;

FIGS. 5A and 5B are screen shots of example user interfaces to a systemfor identification of a chemical compound using a neutral loss method;

FIG. 6 is a block diagram of an example network environment foridentification of polymer species from mass spectrometry output;

FIG. 7 is a block diagram of an example computing device and an examplemobile computing device; and

FIGS. 8A through 8D are a series of screen shots demonstrating anexample use of a system for identification of a chemical compound usinga neutral loss method.

The features and advantages of the present disclosure will become moreapparent from the detailed description set forth below when taken inconjunction with the drawings, in which like reference charactersidentify corresponding elements throughout. In the drawings, likereference numbers generally indicate identical, functionally similar,and/or structurally similar elements.

DETAILED DESCRIPTION

Throughout the description, where apparatus, devices, and systems aredescribed as having, including, or comprising specific components, orwhere processes and methods are described as having, including, orcomprising specific steps, it is contemplated that, additionally, thereare apparatus, devices, and systems of the present invention thatconsist essentially of, or consist of, the recited components, and thatthere are processes and methods according to the present invention thatconsist essentially of, or consist of, the recited steps.

It should be understood that the order of steps or order for performingcertain action is immaterial so long as the invention remains operable.Moreover, two or more steps or actions may be conducted simultaneously.

The term “polymer,” as used herein, refers to a molecule of highrelative molecular mass, the structure of which includes the multiplerepetition of units derived, actually or conceptually, from atoms. Insome embodiments, a polymer has an average molecular weight greater thanabout 100 Da. In some embodiments, a polymer has an average molecularweight greater than about 250 Da. In some embodiments, a polymer has anaverage molecular weight greater than about 500 Da. In some embodiments,a polymer has an average molecular weight greater than about 1,000 Da.In some embodiments, a polymer has an average molecular weight greaterthan about 10,000 Da.

The term “repeating unit”, as used herein, refers to a moiety thatoccurs at least one time in a polymer molecule. In some embodiments, arepeating unit in a polymer has the same molecular weight as a monomerused to form the polymer.

The term “end group” as used herein, refers to a chemical formulaincluding the polymer molecule, but not within the repeating units. Insome embodiments end groups are the end groups of a linear polymer, inother embodiments, the end groups of the current context may representside chains of a linear or cyclic polymer. End groups can be smaller orlarger than repeating units.

As part of establishing a compound's identity using mass spectrometrydata, some existing software packages involve referencing establisheddatabases when analyzing candidate molecular formulas. For example,Applicant's AxION EC ID (Elemental Composition Identification) softwarepackage allows the user to determine the composition of known (“knownunknowns”) and unknown (“unknown unknowns”) compounds from a sampleanalysis. The program calculates the elemental composition of theanalyte based on the measured exact mass of the observed molecular ionand the relative abundance of the isotope ratios in the molecular ionisotopic distribution. AxION EC ID then calculates potential molecularformulas for the analyte, links to the PubChem Compound database, andlists all possible compounds (and associated structures) for thatcomposition.

In certain embodiments, the present invention encompasses the findingthat, notwithstanding the excellent results achieved with the existingelemental composition identification software for molecules ofrelatively low molecular weight (e.g., less than 1000 Da), the abilityto efficiently identify molecules of higher molecular weight (e.g., apolymer) is highly desirable. As described herein, the presentdisclosure provides, among other things, a method including the step ofpredicting the structure or exact mass of a polymer molecule, whereinthe method includes a user input of one or more known repeating units inthe polymer molecule. Although the precise identity of the polymer endgroups may not be known, the input of one or more known repeating unitsis sufficient to predict structures or exact masses of parent moleculeswith a high degree of accuracy.

Any polymeric molecule containing one or more repeating units may beused in accordance with the provided methods. In some embodiments, apolymer analyzed by the provided methods is a homopolymer including onlyone repeating unit. In some embodiments, a polymer analyzed by theprovided methods is a copolymer including two or more differentrepeating units.

In certain embodiments, a polymer analyzed by the provided methods isselected from the group consisting of a polyglycoside, a polynucleotide,a polypeptide, a polycarbonate, a polyamide, a polyolefin, a polyether,a siloxane, a polyacetal, a polyketal, a polyorthoester, a polyester, apolyaramide, and derivatives thereof.

In certain embodiments, a polymer analyzed by the provided methods isselected from the group consisting of a polysaccharide, a glycopeptide,a glycolipid, and derivatives thereof. In some embodiments, a polymeranalyzed by the provided methods is a homopolysaccharide selected fromthe group consisting of cellulose, amylose, dextran, levan, fucoidan,carraginan, inulin, pectin, amylopectin, glycogen and lixenan. In someembodiments, a polymer analyzed by the provided methods is aheteropolysaccharide selected from the group consisting of agarose,hyluronan, chondroitinsulfate, dermatansulfate, keratansulfate, alginicacid and heparin. In certain embodiments, such polysaccharides may bemodified (e.g., bear protecting groups or contain ring-opened units fromtreatment with oxidizing reagents).

In some embodiments, a polymer analyzed by the provided methods isselected from the group consisting of poly(ethylene carbonate),poly(propylene carbonate), poly(propylene carbonate)-co-poly(ethylenecarbonate), poly(butylene carbonate), poly(cyclohexene carbonate),poly(limonene carbonate), and poly(1,2 hexene carbonate).

In some embodiments, a polymer analyzed by the provided methods is apolyamide selected from the group consisting of nylon-6, nylon-6,6,nylon-12, nylon-12,12, and nylon-11.

In some embodiments, a polymer analyzed by the provided methods isselected from the group consisting of a polyethylene, apoly(tetrafluoroethylene), a polypropylene, a polyisobutylene, apolystyrene, a polyacrylonitrile, a poly(vinyl chloride), a poly(methylacrylate), a poly(methyl methacrylate), a polybutadiene, apolychloroprene, a poly(cis-1,4-isoprene), and apoly(trans-1,4-isoprene).

In some embodiments, a polymer analyzed by the provided methods isselected from the group consisting of poly(lactic acid), thermoplasticstarch, poly(3-hydroxybutyrate), poly(4-hydroxybutyrate), poly(3-hydroxypropionate), polyhydroxyoctanoate, poly(3-hydroxyvalerate),poly(3-hydroxybutyrate-co-3-hydroxyvalerate), poly(ethyleneterephthalate) (PET), poly(butylene terephthalate), biodegradablepolyesters like poly(butylene adipate), poly(ethylene adipate),poly(butylene succinate), poly(butylene adipate-co-terephtalate),poly(butylene adipate-co-butylene succinate), poly(butyleneadipate-co-terephtalate), other aliphatic and aromatic polyesters,poly(vinyl alcohol), poly(vinyl acetate), ethylene vinyl alcohol polymer(EVOH), poly(caprolactone), poly(ethylene glycol), poly(proplyeneglycol), polyoxymethylene, polyether ether ketone, poly(tetramethyleneether) glycol, and polyesteramide.

In some embodiments, a polymer analyzed by the provided methods is alinear polymer. In some embodiments, a polymer analyzed by the providedmethods is a cyclic polymer. In some embodiments, a polymer analyzed bythe provided methods is a branched polymer. In some embodiments, apolymer analyzed by the provided methods is a globular polymer. In someembodiments, a polymer analyzed by the provided methods is a graftcopolymer. In some embodiments, a polymer analyzed by the providedmethods is a comb copolymer.

Any repeating unit of a polymer can be used in accordance with theprovided methods. In some embodiments, such a repeating unit iscontained in a polymer described above. In certain embodiments,repeating units are the smallest possible units of monomers that form apolymer. For example, a repeating unit of polydimethylsiloxane is—Si(CH₃)₂O—. In certain embodiments, a repeating unit can include two ormore monomeric units, such as in the copolymer polyethylene carbonatewhere a repeating unit can be —OC(O)O(CH₂)₂—, or the repeating unit maybe further broken into monomers —OC(O)— and —O(CH₂)₂—.

In some implementations, the present disclosure may be directed to asystem and method for identification of polymer species from massspectrometry output. To aid in the speed and accuracy of identificationof a chemical compound including a unit that repeats one or more times,such as a polymer structure, a software tool for chemical formulaidentification may be provided with one or more candidate repeatingunits as an input. In some implementations, the software tool may beused to identify polymeric species having a repeating unit that repeatsat least three times. Using the repeating units, the software tool mayiteratively search for a chemical formula including the repeating unitsplus an end unit structure matching the mass spectrometry output withina threshold tolerance.

In some implementations, the software tool accepts both a repeating unitstructure and a limitation upon the structure of one or both endunit(s). For example, a user may limit the number of elements composingeach end unit to a maximum number of elements (e.g., 10). In anotherexample, a user may limit the type of elements to a particular set.

The present disclosure, in some implementations, may be directed to asystem and method for identification of a chemical formula of a samplecompound based at least in part upon the neutral loss. Herein, neutralloss refers to a mass difference between two mass spectral peaks due toprocessing such as front-end chemistry or gas-phase fragmentation whichresults in a loss of molecular formula. The loss of molecular formulacan be attributed to at least one existing and reported neutral, stablemolecule. A majority of gas phase collisionally induced disassociationreactions, for example which proceed via a unimolecular decay slightlyabove the apparent activation barrier on a kinetic scale oftime-of-flight (TOF) mass spectrometry with ion activation in the ionguide or in the ion source, will demonstrate neutral loss. In thesereactions, for example, the mass difference between the product and areactant equals a mass of some known (e.g., reported and stored in achemical formula database) neutral molecule. In determining a degree ofmatching between a candidate chemical formula and features of a chemicalformula identified through mass spectrometry data, a neutral lossanalyzer compares, for each spectral peak within the mass spectrometrydata, observed mass difference (e.g., between a spectral peak and anadjacent spectral peak) with mass molecular compositions within adatabase of chemical formulas. The method of neutral loss analysis, forexample, may increase accuracy or narrow a pool of potential candidatesof chemical formulas in relation to the following examples:collision-induced disassociation in capillary-skimmer region of a TOFmass spectrometer; metabolites in bulk; front-end chemical reactions(e.g., reactions in a sample prior to mass spectrometry); and polymeridentity in relation to exact mass of the repeating unit.

Turning to FIG. 1, an example system 100 for identification of chemicalformulas from mass spectrometry output is illustrated. The system 100includes a spectrometry data analysis server 102 configured to analyze aset of mass spectrometry data 110 generated by a mass spectrometer 104to identify one or more chemical formulas based upon a comparison ofinformation derived from the mass spectrometry data 110 to informationcontained within a chemical formula data store 106. A user may interfacewith the spectrometry data analysis server 102 via a computing device108 (e.g., a computing device locally or remotely connected to the dataanalysis server 102 or input/output (I/O) peripheral devices connecteddirectly to the spectrometry data analysis server 102).

In some implementations, a user operating the computing device 108accesses a mass spectrometry data analyzer 112 executing upon thespectrometry data analysis server 102. In some implementations, the usersupplies the mass spectrometry data 110 generated by the massspectrometer 104 to the mass spectrometry data analyzer 112. The user,in other implementations, selects the mass spectrometry data 110 fromavailable mass spectrometry data (e.g., previously downloaded,transferred, or otherwise made available to the data analysis server 102by the mass spectrometer 104). In some implementations, the massspectrometer 104 includes the data analysis server. For example, thedata analysis server 102 may be implemented as one or more computerprocessors functioning within a mass spectrometer system.

In some implementations, the mass spectrometry data analyzer 110calculates additional data from the mass spectrometry data 110. Forexample, based upon the experimental information contained within themass spectrometry data 110, a mass-charge ratio of ions (e.g.,calculated as centroids of the peaks in the so-called “profile”spectra), the relative intensities of the peaks, and/or electric charge(e.g., based on relative position of peaks believed to be representingthe same isotope cluster).

In addition to the mass spectrometry data 110, in some implementations,the user supplies the mass spectrometry data analyzer 112 with setupdata 116. The setup data 116, in some examples, includes the selectionof one or more functions of the mass spectrometry data analyzer 112 suchas, in some examples, a chemical formula identifier 112 a, a formulagenerator 112 b, and a neutral loss calculator 112 c.

The chemical formula identifier 112 a, for example, may analyze the massspectrometry data 110 to determine one or more chemical formulas thatinclude spectrometry features similar to (e.g., within a thresholddistance from) features of the mass spectrometry data 110. The setupdata 116 provided to the chemical formula identifier 112 a may include,in some examples, accurate mass of the monoisotopic peak, a chargecarrier, isotope abundances, and/or a database of chemical formulas foridentifying candidate chemical formulas. A user may provide, for examplethrough importing mass spectrometry data or by inputting data by hand,an accurate mass of the monoisotopic peak. The accurate mass of themonoisotopic peak, for example, may be taken as a centroid of theprofile peak of the mass spectrometry output. Based upon the massspectrometry output, in some implementations, a default charge carriermay be selected. In other implementations, the user may select a chargecarrier (for example, based upon experimental data or the anticipatedcontent of the experimental chemical compound). Isotope abundances(e.g., relative or absolute intensities of respective peaks in thespectrum), in some implementations, are imported by the chemical formulaidentifier 112 a from the mass spectrometry data 110. A chemical formuladatabase, in some examples, may include the PubChem Compound databasemaintained by the National Center for Biotechnology Information (NCBI)or the molecular spectral databases maintained by the National Instituteof Standards and Technology (NIST). In some implementations, the massspectrometry data analyzer 112 may set a default chemical formuladatabase (e.g., a built-in database or a particular public database).

In some implementations, a threshold variance setting limits the numberof candidate chemical formulas. The user, in some implementations, mayset a parts-per-million (ppM) error cutoff. In some examples, the ppMerror cutoff may be set as 20 ppM, 10 ppM, 5 ppM, or 3 ppM. In someimplementations, the ppM cutoff is selected based upon the type of massspectrometry analysis performed. For example, for a time-of-flight massspectrometer, a reasonable ppM cutoff of 3 ppM may be entered. In someimplementations, the chemical formula identifier 112 a determines theppM cutoff, for example based upon information contained within the massspectrometry data 110. In other implementations, the user may set theppM cutoff error.

The chemical formula identifier 112 a, in some implementations,iteratively searches chemical formulas to identify a structure includingsimilar data (e.g., relative atomic mass, similar total number ofisotopes, similar relative intensity of isotopes, etc.) to the massspectrometry data 110. The setup data provided to the chemical formulaidentifier 112 a, for example, may include a subset of elements, whereany candidate chemical formulas are limited to those composed of thesubset of elements. Instead of or in addition to the subset of elements,in another example, the setup data 116 may include a maximum number ofelements, where candidate chemical formulas are limited to thosecomposed of a total number of elements less than or equal to the maximumnumber of elements. In a further example, the setup data 116 may includea candidate charge carrier for the chemical formula.

The chemical formula identifier 112 a identifies one or more candidatechemical formulas, in some implementations, based upon a mass of anexperimental compound (e.g., as determined from the mass spectrometrydata 110) and a pre-determined set of elements (e.g., C, H, F, O, N, Si,etc.). Using this information, for example, the mass of a candidatechemical formula can be calculated as a sum of the atomic masses of asubset of the predetermined set elements, where each of the subset ofelements may be included within the candidate chemical formula one ormore times. In some implementations, nested loops of summations are usedto iterate through all possible combinations of elements in order toidentify combinations having a mass within a threshold distance of themass of the experimental compound.

In some implementations, the setup data 116 may include a candidatemoiety. The candidate moiety, in some implementations, is selected bythe user from a set of candidate moieties 120, for example, derived fromchemical formula data 118 retrieved from the chemical formula data store106. The chemical formula data store 106, for example, may include adatabase such as the PubChem Compound database maintained by theNational Center for Biotechnology Information (NCBI), containing about26 million chemical compounds and 1.3 million unique molecular formulas.In another example, the chemical formula data store 106 may include adatabase such as the molecular spectral databases maintained by theNational Institute of Standards and Technology (NIST). In otherimplementations, the user inputs (e.g., types in, draws a chemicalformula, drags and drops a chemical formula, etc.) a candidate chemicalmoiety.

The candidate moiety, in some implementations, is designated by the useras a repeating unit for a chemical formula composed of a repeating unitplus end units. The repeating unit, for example, may be a repeating unitof a known polymer. In response to identifying the mass spectrometrydata 110 as including repeating units (e.g., in setup data 116 orprevious data provided by the user of the computing device 108), in someimplementations, candidate moieties 120 may be derived based uponestimated molecular weights of the repeating unit. For example, knowingthat the sample includes a polymeric species with a repeating unit, arelative mass of a repeating unit portion of the polymeric species maybe estimated from the mass spectrometry data. In a particular example,the mass spectrometer output includes a spectral pattern characteristicof a polymer with a repeating unit with a molecular mass ofapproximately 76 dalton (Da). A manual or partially automatedidentification method may be used to match the molecular mass of 76 Dato the candidate repeating unit of polydimethylsiloxanes (e.g.,C2H6SiO).

In the circumstance of the candidate moiety identifying a repeatingunit, in some implementations, the formula generator 112 b may beinvoked to determine one or more matching chemical formulas includingthe candidate repeating unit plus an end unit structure. The user, insome implementations, is provided the opportunity to limit the chemicalformula of the end unit, for example to increase the speed and/oraccuracy of chemical formulas identified by the software tool. In oneexample, the user limits the end unit to a maximum number of chemicalelements (e.g., as designated by the setup data 116). In someimplementations, the formula generator 112 b assumes an identicalchemical composition of each end unit. In other implementations, thechemical composition of each end unit may vary.

The formula generator 112 b determines one or more candidate chemicalformulas 122 (e.g., candidate polymer structures) based in part upon thecandidate moiety 120. Similar to the functionality of the chemicalformula identifier 112 a, the formula generator 112 b identifies one ormore candidate chemical formulas, in some implementations, based upon amass of an experimental compound (e.g., as determined from the massspectrometry data 110) and a pre-determined set of elements (e.g., C, H,F, O, N, Si, etc.). However, because a large portion of the mass of theexperimental compound is immediately accounted for based upon thecandidate moiety 120, only the composition of the each of the end groupsof the experimental chemical formula needs to be determined. In someimplementations, the user identifies an estimated number of iterationsof the candidate moiety 120 included within the experimental chemicalcompound. In other implementations, the formula generator 112 bdetermines a default number of iterations of the candidate moiety 120(e.g., the maximum number of iterations of the mass of the candidatemoiety 120 that does not exceed the mass of the experimental chemicalcompound, as determined via the mass spectrometry data 110). In someimplementations, rather than identifying a candidate moiety, the formulagenerator 112 b is provided an estimated mass of the repeating unit ortotal mass of the repeating unit (e.g., including all iterationsinvolved within the experimental chemical compound). Either way, basedupon the provided information, the formula generator 112 b may begin theidentification process with a “super atom” of a known mass.

Additionally, because the formula generator 112 b is identifyingpotential end group combinations built upon the candidate moiety 120,the maximum number of each type of element in the predetermined set ofelements may be greatly reduced in comparison to the algorithm used bythe chemical formula identifier 112 a. For example, the end groups oflarge biomolecules, such as polymers, may be assumed to contain nogreater than X number of each of the predetermined set of chemicalelements, where X may vary from element to element based upon knownchemistry. In some implementations, the user is provided the opportunityto set a maximum number of each chemical element to include within theend groups of the candidate chemical formulas. The maximum number ofeach element of the predetermined set of elements, in someimplementations, may be identified as a default setting within theformula generator 112 b.

In some implementations, in addition to or instead of determiningcandidate chemical formulas based upon a repeating unit, the massspectrometry data analyzer 112 is configured to identify one or morecandidate chemical formulas based upon a neutral loss estimate. Theneutral loss calculator 112 c, in some implementations, may analyze acandidate chemical formula in view of the mass spectrometry data 110 toidentify a potential match based upon a neutral loss theory. Herein,neutral loss refers to a mass difference between two mass spectral peaksdue to processing such as front-end chemistry or gas-phase fragmentationwhich results with a loss of molecular formula. The loss of molecularformula can be attributed to at least one existing and reported neutral,stable molecule. The neutral loss calculator 112 c, in someimplementations, receives one or more candidate chemical formulas fromthe chemical formula identifier 112 a or the formula generator 112 b.For example, the neutral loss calculator 112 c may be used to refineresultant candidate chemical formulas. In analyzing candidate chemicalformulas based upon neutral loss calculations, for example, an initiallist of candidate chemical formulas may be narrowed to provide moreaccurate results and/or re-prioritized to promote a candidate chemicalformula best matching the mass spectrometry data 110 in relation to theneutral loss concept.

The neutral loss calculator 112 c analyzes the candidate chemicalformula(s) for a potential match with the mass spectrometry data 110based upon the mass difference in principle between any two massspectral peaks. In some implementations, the neutral loss calculatorreceives one or more candidate chemical formulas from the chemicalformula identifier 112 a or the formula generator 112 b. The user, insome implementations, selects a candidate chemical formula for neutralloss calculation. The candidate chemical formula, in some examples, maybe selected from a results list provided via the chemical formulaidentifier 112 a or the formula generator 112 b, through selecting achemical formula from a database, and/or by manually entering acandidate chemical formula.

Beginning with the experimental compound (e.g., as identified within themass spectrometry data 110) and a particular candidate chemical formula,the neutral loss calculator 112 c, in some implementations, identifiesspectral data associated with the candidate chemical formula. Thespectral data, for example, may be obtained through the chemicalstructure data 118. The neutral loss calculator 112 c, in someimplementations, searches the peak list of the spectrum included withinthe mass spectrometry data 110, calculating mass difference between thetheoretical mass of the candidate chemical formula spectrum andexperimental mass of all other spectral peaks obtained from the massspectrometry data 110. For each spectral peak, for example, the neutralloss calculator 112 c may compare the difference (e.g., calculatedbetween the candidate chemical formula spectrum and the observed massobtained from the mass spectrometry data 110) with masses of knownmolecular compositions. The molecular compositions, for example, may beidentified as neutral molecular compositions. The molecularcompositions, in some implementations, are obtained from a database suchas the PubChem Compound database maintained by NCBI or the molecularspectral databases maintained by NIST. The user, in someimplementations, provides a list of neutral molecular compositions ornarrows an initial list of neutral molecular compositions.

As a result of the comparison implemented by the neutral loss calculator112 c, in some implementations, one or more potential neutral lossmatches may be identified. Identification of a neutral loss match, forexample, may be based upon the difference between the experimentalneutral loss and theoretical mass of a particular molecular compositionbeing less than a mass measurement accuracy threshold. The massmeasurement accuracy threshold, for example, may include a defaultsetting or a setting supplied by a user. Additionally, in someimplementations, the neutral loss calculator 112 c may determine thatthe stoichiometry of the particular molecular composition allows for aproposed neutral loss candidate. This determination, for example, may bebased upon the number of elements of each type of element composing theparticular molecular composition being less than or equal to a number ofatoms of this type of element occurring in the candidate chemicalformula.

In some implementations, instead of attempting to make an assignment ofchemical formula candidates to all spectral peaks in the peak list, theneutral loss calculator 112 c attempts to assign neutral losses to massdifference between a particular molecular composition and observed(potential) fragments.

Upon identifying one or more candidate chemical formulas 122, in someimplementations, the mass spectrometry data analyzer 112 presents thecandidate chemical formulas 122 to the user. For example, the user maybe provided a series of selectable chemical formulas, such as a firstpolymer structure 122 a, within a graphical user interface of thecomputing device 108. In addition to a listing of chemical formulas, insome implementations, the mass spectrometry data analyzer 112 provides anumeric and/or graphical comparison of the mass spectrometry data tomass spectrometry values of the candidate chemical formulas. Uponselecting one of the candidate chemical formulas, for example, datarelated to the selected chemical formula may be overlaid upon agraphical analysis of the mass spectrometry data. Metrics, in anotherexample, may be presented to the user, illustrating a margin of errorbetween spectral features of the mass spectrometry data 110 and thecandidate chemical formulas 122. Example user interfaces for providingthe setup data 116 and reviewing the candidate chemical formulas 122 areillustrated in relation to FIGS. 3A through 3F.

FIGS. 2A through 2C are flow charts of example methods foridentification of polymer species from mass spectrometry output. In someimplementations, the example methods may be performed by the formulagenerator 112 b, described in relation to FIG. 1.

Turning to FIG. 2A, a first method 200 for identification of polymerspecies from mass spectrometry output involves determining one or morecandidate compounds based in part upon a mass of a repeating unitportion of an experimental polymer compound.

In some implementations, the method 200 begins with obtaining massspectrometry data of the experimental compound (202).

In some implementations, a mass of the experimental compound isidentified (204).

In some implementations, a mass of the repeating unit is determined(206).

In some implementations, one or more candidate compounds are identified(208).

In some implementations, the one or more candidate compounds areprovided for display to a user (210).

Although the method 200 is illustrated as a particular series of steps,in some implementations, more or fewer steps may be included.Furthermore, in some implementations, one or more of the steps may beexecuted in a different order than described above. Other modificationsare possible without diverging from the spirit and scope of the method200.

Turning to FIGS. 2B and 2C, a second method 220 for identification ofpolymer species from mass spectrometry output involves identifyingpossible atom types for the end units of the polymer and calculatingcombinations of atoms to identify candidate polymer formulas within athreshold distance of an experimental mass of the experimental chemicalcompound.

In some implementations, the method 220 begins with receiving setup data(222).

In some implementations, a set of possible atom types included in theend units of candidate chemical formulas are identified (224).

In some implementations, a maximum number associated with each atom typeof the set of possible atom types is identified (226).

In some implementations, a target mass of the experimental chemicalcompound is identified (228).

If a chemical formula of a repeating unit is provided as input to themethod 220 (230), in some implementations, a theoretical mass of therepeating unit portion is calculated (232). The calculation, in someimplementations, may involve multiplying the mass of the repeating unitby a number of iterations of the repeating unit. If a number ofrepetitions was not specified as input, in some implementations, themethod 220 may identify an initial number of repetitions.

If a chemical formula of the repeating unit has not been provided (230),a mass of the repeating unit portion is identified (234). In thiscircumstance, the mass of the repeating unit portion identifies a totalmass of the repeating unit, including all repetitions of the repeatingunit.

In some implementations, combinations of possible atom types areiteratively summed in order to identify all potential combinations ofend types with a total mass (including the mass of the repeating unitportion), within a threshold of the target mass of the experimentalcompound (236).

In some implementations, if the estimated mass of the repeating unit wasinitially calculated based upon a provided chemical formula (238), andthe target mass of the experimental compound was exceeded in one or moreof the iterations (240), a number of repetitions of the target chemicalformula of the repeating unit is decremented (242).

The method 220, at this point, returns to calculating the theoreticalmass of the repeating unit portion using the reduced number ofrepetitions (232). For example, if, in the first loop, the theoreticalmass of the repeating unit portion was calculated using seven instancesof the repeating unit chemical formula mass, the second loop willinvolve calculating the theoretical mass based upon six instances of therepeating unit chemical formula mass.

Turning to FIG. 2C, upon completion of all iterations, in someimplementations, candidate chemical formulas are identified from the setof potential combinations (244).

In some implementations, mass spectrometry data of the experimentalcompound is obtained (246).

In some implementations, theoretical mass-spectral data for a candidatechemical formula is identified (248).

In some implementations, the theoretical mass-spectral data of thecandidate chemical formula is compared to the mass spectrometry data(250).

If additional candidate compounds were identified (252), for eachcandidate chemical compound, steps (248) and (250) are repeated.

In some implementations, the candidate chemical formulas are rankedbased at least in part upon the comparison (254).

In some implementations, the candidate chemical formulas are presentedto a user (256).

Although the method 220 is illustrated as a particular series of steps,in some implementations, more or fewer steps may be included.Furthermore, in some implementations, one or more of the steps may beexecuted in a different order than described above. Other modificationsare possible without diverging from the spirit and scope of the method220.

FIGS. 3A through 3F are screen shots of an example user interface to asystem for identification of polymer species from mass spectrometryoutput. The screen shots depict a series of user interactions involvingmatching an experimental chemical formula known to be a polymercontaining the repeating unit C₂H₆SiO.

The repeating unit C₂H₆SiO, in some examples, may be selected by a user(e.g., from a list of common chemical formulas provided by the programfor selection or a list of user-specific chemical formulas input intothe system previously by the user, etc.), dragged and dropped by a userfrom a separate module, or entered by the user (e.g., in a text entrycontrol). In some implementations, rather than entering the chemicalformula of a repeating unit, the user has the option of entering a massof the repeating unit. The entry method of the repeating unit may varydepending upon the circumstances. For example, in many cases, such asanalytical studies of synthetic products, a researcher has sufficientknowledge of the sample history (e.g., synthesis design) to simply usethe program to verify that the anticipated polymer was built or todetermine the composition of the end units of a known (or suspected)polymeric repeating unit.

Turning to FIG. 3A, a main window 300 illustrates a first set ofcandidate chemical formulas 302. The first set of candidate chemicalformulas 302 were initially selected by setting a charge carrier 304 ofa proton (H+) and selecting a “Find Formulae” control 306. For example,the first set of candidate chemical formulas 302 may have been derivedusing the chemical formula identifier 112 a as described in relation toFIG. 1. Each candidate formula 302 of the first set of candidatechemical formulas 302, in some implementations, is identified based upona mass M_(experimental) of the experimental compound and apre-determined set of a number K of elements (e.g., C, H, F, O, N, Si,etc.). In some implementations, the user may specify the pre-determinedset of K elements. For example, through selection of an elements control314, an element selection interface may be presented to the user toidentify a subset of chemical elements to include in candidate chemicalformulas. For example, selection of the elements control 314 may resultin presentation of a graphical representation of a periodic table.Through selecting individual elements, the user may allow and/ordisallow particular elements from being included within the candidatechemical formulas.

Using the mass of the experimental compound and the pre-determined setof K elements, for example, the mass M_(i) of a candidate molecule canbe calculated as a sum of the atomic masses of a subset of the Kelements multiplied individually by a number n_(k) of elements of eachtype (e.g., C, H, F, O, N. Si, etc.) In some implementations, nestedloops of summations (e.g., n=0, 1, . . . , N, k=0, 1, . . . , K) areused to iterate through all possible combinations of elements in orderto identify combinations having a mass within a threshold distance ofthe mass M_(experimental). Various algorithmic implementations mayinvolve, in some examples, hard loops, recursion, and/or sorting byatom-type mass before determining iterative structure.

In some implementations, the algorithm may involve a number of rulesand/or limitations, for example based on general chemistry, to restrictthe number of iterations involved in the candidate chemical formulaselection. For example, the set of K elements may be restricted tocertain elements or element types. In another example, one element typemay be related to another element type (e.g., if element X is used, donot consider chemical formulas involving element Y, or vice-versa).

Regardless the particular implementation of the chemical formulacandidate identification algorithm, the iterative approach ofidentifying candidates based upon the mass M_(experimental) of theexperimental compound and the pre-determined set of a number K ofelements inherently suffers from exponential nature of dependence ofcomputational steps involved on (1) the target ion, and (2) the numberof atom types allowed for consideration. The resultant candidateformulas illustrated within FIG. 3A illustrate the limitations of thisapproach for candidate formula identification. The first set ofcandidate chemical formulas 302, for example, where selected from the“Compound (PC)” database, as selected within a chemical formula databasedrop-down menu 312. The first set of candidate chemical formulas 302contain a first candidate chemical formula 302 a of C40H32ClNOS and asecond candidate chemical formula 302 b of C38H25N5Ni. As can been seenby the first set of candidate chemical formulas 302, neither candidatechemical formula 302 contains the repeating unit of the polymer (e.g.,C₂H₆SiO). Thus, FIG. 3A illustrates the potential for error whenattempting to determine a large mass polymer using a strictly iterativematching solution.

The candidate chemical formulas 302, in some implementations, may bedisplayed in a ranked order, for example based upon a closest similaritybetween the spectral pattern, mass, and other information within themass spectrometry data of the experimental chemical formula and dataregarding each candidate chemical formula, for example as supplied bythe database elected within the drop-down menu 312. Each candidatechemical formula 302, as illustrated, is associated with a respectivescore 303 and a respective parts-per-million error (ppM-error) 305. TheppM-error 305, for example, may be determined based upon a comparison ofuser-specified experimentally observed mass of the monoisotopic peak tothe candidate chemical formula mass data (e.g., as obtained from achemical formula database). In some implementations, the ppM-error 305,upon calculation, may then be used by the system (e.g., the massspectrometry data analyzer 112 described in relation to FIG. 1) tonarrow the resultant candidates. For example, for each candidatechemical formula having a ppM-error 305 outside a ppM error cutoff 307may be discarded from the results list.

The respective score 303, in some implementations, is calculated in amanner to separate the valuation of the candidate from dependence upondata provided by a particular database. For example, the respectivescore 303 may initially be based upon an experimentally observed mass ofthe monoisotopic peak in view of a mass accuracy cutoff (e.g., as set bythe system by default or as provided by the user, for example within appM error cutoff field 307 of the main window 300), in a mass errordistribution function scaled to unity. The respective score 303, inother words, will have a value of one to indicate a perfect matchbetween experimental data and a candidate chemical formula. A candidatechemical formula with a mass residual other than zero, in this example,will be awarded a respective score 303 that is less than one.

In some implementations, the respective score 303 is further refined byconstructing a second scoring value relative to isotope abundances. Acumulative absolute error cutoff (e.g., a default value provided by thesystem or a value specified by the user within an iso cum-sigma datafield 309), for example, may used as a sigma-parameter of a one-sidedunity-scaled zero-centered normal distribution. The difference inrelative isotope abundances, when taken in view of the distribution, mayprovide a relative measure matching between theoretically calculatedrelative abundance of isotopes of a candidate molecular formula and theexperimental relative abundance of isotopes. In combining both themass-based scoring value and the isotope-abundance based scoring value,in some implementations, the respective score 303 is obtained. Forexample, the two values may be treated as orthogonal coordinates tocalculate the final score, as a Euclidian distance scaled to unity.

In some implementations, selection of a defaults control 313 may resultin setting one or more of a default charge carrier, a default ppM errorcutoff 307, and/or a default iso cum-sigma percentage 309.

In a graphical comparison pane 308, an experimental spectral pattern 310a is overlaid with a candidate pattern 310 b. The candidate pattern 310b, for example, illustrates a spectral pattern of a first candidatechemical formula 302 a of C40H32ClNOS (e.g., illustrated as highlightedabove within the list of candidate chemical formulas 302).

In some implementations, values of the experimental spectral pattern 310a relate to a set of isotope abundances 311 illustrated above thegraphical comparison pane 308. The isotope abundances 311, in someimplementations, may be automatically identified, for example based uponmass spectrometry data provided to the system. For example, for eachisotopic peak within a provided spectrum, the system (e.g., massspectrometry analyzer 112 as described in relation to FIG. 1) may importa respective isotope abundance 311. The user, in some implementations,may be provided the opportunity to manually enter or manually adjust theisotope abundances 311.

In addition to the graphic illustration provided within the graphicalcomparison pane 308, in some implementations, detailed informationregarding the experimental spectral pattern 310 a in comparison to thecandidate pattern 310 b of C40H32ClNOS is provided. For example, turningto FIG. 3B, a formula statistics window 316 is presented next to themain window 300. The formula statistics window 316 provides an absoluteerror distance graph 318, a ppM-scores graph 320, and an iso-scoresgraph 322. As discussed above in relation to the scores 303 of FIG. 3A,the scores 303, in some implementations, illustrate a combination of amass error score and an isotope abundance error score. For example, theabsolute error distance graph 318 may illustrate a relative value of afirst portion (e.g., isotope error) of the score 303 a, while theppM-scores graph 320 may illustrate the relative value of a secondportion (e.g., mass error) of the score 303 a. The iso-scores graph 322,in this example, may illustrate a graphical representation of the score303 a (e.g., 0.655).

Turning to FIG. 3C, a formula generator window 330, in someimplementations, is used to supply setup data regarding a chemicalformula involving a repeating unit plus end units. The formula generatorwindow 300, for example, may be used to provide setup data 116 to theformula generator 112 b, as described in relation to FIG. 1. The formulagenerator window 330 includes a drop-down menu 332 for selecting arepeating unit. The contents of the drop-down menu 332, for example, maybe selected based in part upon a target mass of the repeating unit, suchas a target mass 334 illustrated above the drop-down menu 332. In someimplementations, the target mass 334 is derived from the massspectrometry data regarding the experimental chemical formula. Althoughillustrated as a drop-down menu 332, in some implementations, a user hasthe option of manually entering the chemical formula of the repeatingunit.

Beneath the drop-down menu 332, a series of chemical elements 334 isillustrated, including a minimum number 338, a maximum number 340, and aTypMax 342. The minimum number 338 and the maximum number 340 may be setto specify a range of the numbers of each element that an end unit ofthe experimental chemical formula may contain. For example, the user hasselected that the end unit may contain between 0 and 2 of each of thefollowing elements: carbon 336 a, fluorine 336 b, hydrogen 336 c,nitrogen 336 d, oxygen 336 e, sulfur 336 f, chlorine 336 g, bromine 336h, iodine 336 i, phosphorus 336 j, and silicon 336 k. The followingelements have not been selected, and thus may not be included withineither end unit: sodium 336 l, potassium 336 m, and calcium 336 n. Thevalues illustrated within the TypMax 342 column, in someimplementations, identify typical maximum values for each chemicalelement, for example derived through chemistry-based restrictions (e.g.,limitations derived via chemistry literature regarding the compositionof the end units of known polymer compounds). The TypMax 342 values, forexample, may be used as a guide by the user when identifying a maximumnumber related to each chemical element.

As illustrated, based upon the settings within the formula generatorwindow 330, the main window 300 contains a new set of candidate chemicalformulas 302, including a first candidate chemical formula 302 a of(C2H6OSi)8H1O1 and a second candidate chemical formula 302 b of(C2H6OSi)7C2F2H1N2. As discussed above in relation to FIG. 3A, eachcandidate chemical formula 302 is associated with a respective score 303and a respective ppM-error 305. Unfortunately, each score 303 andppM-error 305 is as bad, if not worse, than the candidate chemicalformulas presented in relation to FIG. 3A. In this circumstance, theuser may revisit the setup data to continue to interact with the programto identify a candidate chemical formula with high likelihood of amatch.

Because the candidate chemical formulas 302 are still not within a rangeof qualifying as a “match,” turning to FIG. 3D, the user may opt todetermine statistics regarding the repeating unit alone to betteridentify a chemical formula of an appropriate end unit. The user may runa comparison of a number of repeating units in relation to theexperimental chemical formula. As illustrated in the formula generatorwindow 330, a chemical formula entry field 350 contains a chemicalformula of (C2H6SiO)8. In other words, the user is determining whethereight repetitions of the repeating unit comes close to the mass of theexperimental chemical formula. The user, for example, may select a“Check” control 352 to obtain results related to the chemical formula of(C2H6SiO)8. As illustrated above the graphical comparison pane 308,responsive to activating the “Check” control 352, a mass 354 of eightrepetitions of the repeating unit structure is calculated as 592.1503Da. Based upon this information, the user may theorize that a chargecarrier of ammonium (NH4+) may be more appropriate than the previouslyattempted charge carrier of a proton (H+). The charge carrier, intypical circumstances, is characteristic of the sample chemistry (e.g.salinity, acidity, etc.), ionization technique type, and mode. Whenworking with known analytes, for example, the charge carrier isimmediately revealed. In the particular example illustrated in FIG. 3D,a literature search may have been conducted by the user to identify thepossibility of NH4+ as a charge carrier.

Turning to FIG. 3E, the charge carrier 304 has been changed to ammonium(NH4+). As illustrated within the main window 300, the list of candidatechemical formulas 302 includes a top-ranked candidate chemical formula302 a of an ammoniated octamer, (C2H6OSi)8.

In some implementations, upon selection of one of the candidate chemicalformulas 302, a chemical structure selector 370 is displayed, providingone or more candidate chemical structures 372. Turning to FIG. 3F, basedupon the candidate chemical formula 302 a of an ammoniated octamer,(C2H6OSi)8, two candidate chemical structures 372 are illustrated. Insome implementations, the chemical structure selector 370 is presentedwithin a separate browsing unit. For example, while the main window 300,formula statistics window 316, and formula generator window 330 may bepresented by the mass spectrometry data analyzer 112, the chemicalstructure selector 370 may be presented by an engine provided by thechemical structure data store 106, such as a commercial database system,government database system, or standards body database system. Thecandidate chemical structures 372 are not necessarily ranked in aparticular order. For example, unless a distinction between candidatechemical structures 372, such as absence of fragmentation or retentiontime, may be used to derive a preference between the candidate chemicalstructures 372, the candidate chemical structures 372 may be consideredto each be equally viable. For example, the user may review other typesof information regarding the structure of a candidate chemical compoundin relation to additional information regarding the experimentalcompound such as, in some examples, gas-phase chemistry, chromatography,and ion mobility.

In some implementations, candidate chemical structures are based atleast in part upon a neutral loss estimate. The neutral loss utilityoutputs a list of candidates molecular formulas for a parent ion, basedon its monoisotopic mass and isotope pattern matched to a database. Theuser may set a tolerance for the measured mass accuracy as well asconfidence in the isotopic ratio measurement. These tolerances enablethe user to filter proposed molecular formulas. Upon selection of one ofthe candidate molecular formulas, the neutral loss utility searches thepeak list of the spectrum, calculating mass difference between thetheoretical mass of the proposed formulas and the experimental mass ofeach of a plurality of spectral peaks. For each spectral peak, observedmass difference is compared with masses of molecular compositions in thedatabase. A potential neutral loss match is reported if (i) thedifference between the experimental neutral loss and theoretical mass ofa molecule is less than the mass measurement accuracy set by the userand (2) stoichiometry of the selected parent ion candidate moleculeallows for a proposed neutral loss candidate, i.e., the number of atomsof each type comprising the neutral loss candidate is equal or greaterin the current parent ion candidate.

FIGS. 4A and 4B illustrate a flow chart of an example method 400 foridentification of a chemical formula based in part upon neutral loss.

In some implementations, the method 400 begins with obtaining candidatechemical formulas (402).

In some implementations, mass spectrometry data of an experimentalchemical compound is obtained (402).

In some implementations, theoretical mass-spectral data for a candidatechemical formula is identified (406).

In some implementations, the mass difference between a theoretical massof the monoisotopic peak of the candidate chemical formula and anexperimental mass of all other spectral peaks is calculated (408).

In some implementations, for each spectral peak, the calculated massdifference is compared with a mass of a number of neutral molecularcompositions (410).

If, during comparison, it is determined that the mass difference inregards to a particular neutral molecular composition is less than amass measurement accuracy setting (412), and it is further determinedthat the stoichiometry of the neutral molecular composition is a matchto the candidate chemical formula (414), the particular neutralmolecular composition, in some implementations, is identified as aneutral loss match (416). In some implementations, two or more neutralmolecular compositions can be identified as neutral loss matches to aparticular candidate chemical formula.

In some implementations, if the method 400 is performed in relation totwo or more candidate chemical formulas (418), for each candidatechemical formula, the steps 406 through 416 may be repeated.

Turning to FIG. 4B, upon conclusion of identifying the one or moreneutral loss matches, in some implementations, the candidate chemicalformulas may be ranked in part based upon the results of the neutralloss matching (420). Rather than or in addition to ranking based in partupon the neutral match results, in some implementations, one or morecandidate chemical formulas may be discarded from the candidate chemicalformulas based upon no neutral loss match being identified.

In some implementations, the candidate chemical formula(s) may bepresented to the user (422). The neutral loss match information, in someimplementation, may be included within the presentation.

Although the method 400 is illustrated as a particular series of steps,in some implementations, more or fewer steps may be included.Furthermore, in some implementations, one or more of the steps may beexecuted in a different order than described above. Other modificationsare possible without diverging from the spirit and scope of the method400.

FIGS. 5A and 5B are screen shots of example user interfaces to a systemfor identification of a chemical compound using a neutral loss method.In some implementations, the screen shots may be generated by the massspectrometry data analyzer 112, described in relation to FIG. 1. Aportion of the information presented in the screen shots, for example,may be produced by the neutral loss calculator 112 c, described inrelation to FIG. 1.

Turning to FIG. 5A, a main window 500 illustrates an example userinterface for identifying one or more candidate chemical formulas basedupon analysis of mass spectrometry data. The identification of thecandidate chemical formulas, in some implementations, includes astraight iterative analysis, for example as described in relation to thechemical formula identifier 112 a of FIG. 1. In some implementations,the identification of the candidate chemical formulas includes ananalysis based upon a mass of a repeating unit portion and theidentification of potential end unit compositions, for example asdescribed in relation to the formula generator 112 b described inrelation to FIG. 1. Upon selecting a “find formulae” control 502 withinthe main window 500, for example, one or more candidate chemicalformulas may be identified. As illustrated, one candidate formula 504was identified.

In the upper right hand corner, a CID (Collision-Induced Dissociation)checkbox 506 has been activated. Due to activation of the CID checkbox506, in some implementations, a neutral loss matching process mayanalyze the candidate chemical formula 504 in relation to the massspectrometry data. The analysis, for example, may include a processsimilar to a portion of the method 400 described in relation to FIG. 4A.

Based upon identification of a potential neutral loss match, in someimplementations, a spectrum interface is presented to the user. Turningto FIG. 5B, a neutral loss spectral analysis screen 520 includes aseries of peaks 522. In relation to the peaks 522, any identifiedfragments matching a neutral molecular composition may be identifiedwith a respective neutral loss formula 524. Note that peak 522 g isassociated with three neutral loss formulas, namely 524 d through 524 f.

The following example examines collision induced dissociation in thecapillary-skimmer region of a TOF (time-of-flight) mass spectrum. A massspectrum of an unknown compound with CID fragmentation is obtained.Using the facility described above in relation to FIGS. 2A to 2C,candidates for the unknown compound are identified. The collisioninduced dissociation (CID) mass spectrum (e.g., theoreticalmass-spectral data) for a selected candidate is presented. The elementalcomposition of the neutral loss for each peak in the mass spectrum ispredicted by searching a database and is displayed. By subtracting theneutral loss proposed elemental composition from the parent (candidate)elemental composition, an elemental composition of each of the measuredmass spectral peaks can be assigned. A check of the mass andstoichiometry can then result in the identification of the candidate asa neutral loss match for the unknown compound.

In FIG. 8A, the graph 800 illustrates mass spectrum information for acompound having the chemical formula C₁₇H₂₁NO₄ with mass measurement of304.1547 and collision induced dissociation (CID) fragmentationobserved. The graph 800 includes a number of spectral peaks, eachspectral peak being associated with a particular amplitude 802 and aparticular mass 804. Without knowing the chemical formula for thecompound having the mass spectrum information illustrated in the graph800, using neutral loss analysis, a matching chemical formula candidatemay be determined.

For example, turning to FIG. 8B, a main window 810 illustrates anexample user interface for identifying one or more candidate chemicalformulas 814 based upon analysis of mass spectrometry data, similar tothe main window 500 described in relation to FIG. 5A. In the upper righthand corner, a CID (Collision-Induced Dissociation) checkbox 812 hasbeen activated. Due to activation of the CID checkbox 812, in someimplementations, a neutral loss matching process may analyze thecandidate chemical formulas 814 in relation to the mass spectrometrydata illustrated in graph 800 of FIG. 8A. The analysis, for example, mayinclude a process similar to a portion of the method 400 described inrelation to FIG. 4A.

Through the neutral loss analysis, turning to FIG. 8C, an example blockdiagram 820 illustrates that the elemental composition of the neutralloss for each peak of the graph 800 may be predicted by searching adatabase for a loss of molecular formula resulting in a neutral, stablemolecule having a chemical composition including a portion of the atomsof a candidate chemical formula 814 a. Example neutral molecular matchesfor the peaks of the graph 800 are illustrated in a first neutral lossmatching graph 822. Selected from the neutral loss matching graph 822, afirst example segment 824 a includes the peak 802 e having a neutralloss molecular match representing a loss of C₇H₆O₂ 828 b. The firstexample segment 824 a additionally includes the peak 802 c having aneutral loss molecular match representing a loss of C₇H₃NO₂ 828 a.Turning to a second example segment 824 b, the peak 8021 has a neutralloss molecular match representing a loss of CH₄O 828 c.

By subtracting the neutral loss proposed elemental composition (e.g.,828 a, 828 b, 828 c) from the parent elemental composition (e.g.,C₁₇H₂₁NO₄ 814 a), an elemental composition corresponding to the measuredmass spectral peak can be assigned. Turning to FIG. 8D, a resultsdiagram 840 includes the graph 800 overlaid with a comparison table 842.For each of four example peaks 802 a. 802 e, 8021, and 802 o, a proposedformula 844 has been matched to the observed mass 804. In the example ofpeak 8021, in subtracting CH₄O 828 c (as identified at peak 802 e ofFIG. 8C) from the parental composition C₁₇H₂₁NO₄ 814 a, a formulaC₁₆H₁₇NO₃ 844 b is determined. Similarly, for peak 802 e, a formulaC₁₀H₁₅NO₂ 844 c is determined by subtracting the chemical compoundC₇H₆O₂ 828 b (of FIG. 8C) from the parental composition C₁₇H₂₁NO₄ 814 a,and a formula C₉H₁₁NO 844 d is determined by subtracting C₈H₁₀NO₃ (notillustrated) from the parental composition C₁₇H₂₁NO₄ 814 a.

In calculating a molecular weight of the proposed formulas 844, arespective expected mass 846 is calculated. In comparing the expectedmass 846 to the observed mass 804, a parts-per-million difference 848 iscalculated. Turning to FIG. 8B, it may be noted that the ppM difference848 in each case is within a specified ppM error range 816.

In certain embodiments, methods described herein use data produced bymass spectrometers with any one or more of a variety of mass analyzers,for example, a time-of-flight analyzer, a sector field mass analyzer, aquadrupole mass analyzer, and/or an ion trap. In certain embodiments,methods employ tandem mass spectrometry, and molecule fragmentation isperformed using, for example, collision-induced dissociation (CID),electron capture dissociation (ECD), electron transfer dissociation(ETD), infrared multiphoton dissociation (IRMPD), blackbody infraredradiative dissociation (BIRD), electron-detachment dissociation (ED)and/or surface-induced dissociation (SID). In certain embodiments,methods described herein are used in combination with chromatographymethods, e.g., GC-MS, LC-MS, and/or IMMS.

As shown in FIG. 6, an implementation of an exemplary cloud computingenvironment 600 for identification of polymer species from massspectrometry output is shown and described. The cloud computingenvironment 600 may include one or more resource providers 602 a, 602 b,602 c (collectively, 602). Each resource provider 602 may includecomputing resources. In some implementations, computing resources mayinclude any hardware and/or software used to process data. For example,computing resources may include hardware and/or software capable ofexecuting algorithms, computer programs, and/or computer applications.In some implementations, exemplary computing resources may includeapplication servers and/or databases with storage and retrievalcapabilities. Each resource provider 602 may be connected to any otherresource provider 602 in the cloud computing environment 600. In someimplementations, the resource providers 602 may be connected over acomputer network 608. Each resource provider 602 may be connected to oneor more computing device 604 a, 604 b, 604 c (collectively, 604), overthe computer network 608.

The cloud computing environment 600 may include a resource manager 606.The resource manager 606 may be connected to the resource providers 602and the computing devices 604 over the computer network 608. In someimplementations, the resource manager 606 may facilitate the provisionof computing resources by one or more resource providers 602 to one ormore computing devices 604. The resource manager 606 may receive arequest for a computing resource from a particular computing device 604.The resource manager 606 may identify one or more resource providers 602capable of providing the computing resource requested by the computingdevice 604. The resource manager 606 may select a resource provider 602to provide the computing resource. The resource manager 606 mayfacilitate a connection between the resource provider 602 and aparticular computing device 604. In some implementations, the resourcemanager 606 may establish a connection between a particular resourceprovider 602 and a particular computing device 604. In someimplementations, the resource manager 606 may redirect a particularcomputing device 604 to a particular resource provider 602 with therequested computing resource.

FIG. 7 shows an example of a computing device 700 and a mobile computingdevice 750 that can be used to implement the techniques described inthis disclosure. The computing device 700 is intended to representvarious forms of digital computers, such as laptops, desktops,workstations, personal digital assistants, servers, blade servers,mainframes, and other appropriate computers. The mobile computing device750 is intended to represent various forms of mobile devices, such aspersonal digital assistants, cellular telephones, smart-phones, andother similar computing devices. The components shown here, theirconnections and relationships, and their functions, are meant to beexamples only, and are not meant to be limiting.

The computing device 700 includes a processor 702, a memory 704, astorage device 706, a high-speed interface 708 connecting to the memory704 and multiple high-speed expansion ports 710, and a low-speedinterface 712 connecting to a low-speed expansion port 714 and thestorage device 706. Each of the processor 702, the memory 704, thestorage device 706, the high-speed interface 708, the high-speedexpansion ports 710, and the low-speed interface 712, are interconnectedusing various busses, and may be mounted on a common motherboard or inother manners as appropriate. The processor 702 can process instructionsfor execution within the computing device 700, including instructionsstored in the memory 704 or on the storage device 706 to displaygraphical information for a GUI on an external input/output device, suchas a display 716 coupled to the high-speed interface 708. In otherimplementations, multiple processors and/or multiple buses may be used,as appropriate, along with multiple memories and types of memory. Also,multiple computing devices may be connected, with each device providingportions of the necessary operations (e.g., as a server bank, a group ofblade servers, or a multi-processor system).

The memory 704 stores information within the computing device 700. Insome implementations, the memory 704 is a volatile memory unit or units.In some implementations, the memory 704 is a non-volatile memory unit orunits. The memory 704 may also be another form of computer-readablemedium, such as a magnetic or optical disk.

The storage device 706 is capable of providing mass storage for thecomputing device 700. In some implementations, the storage device 706may be or contain a computer-readable medium, such as a floppy diskdevice, a hard disk device, an optical disk device, or a tape device, aflash memory or other similar solid state memory device, or an array ofdevices, including devices in a storage area network or otherconfigurations. Instructions can be stored in an information carrier.The instructions, when executed by one or more processing devices (forexample, processor 702), perform one or more methods, such as thosedescribed above. The instructions can also be stored by one or morestorage devices such as computer- or machine-readable mediums (forexample, the memory 704, the storage device 706, or memory on theprocessor 702).

The high-speed interface 708 manages bandwidth-intensive operations forthe computing device 700, while the low-speed interface 712 manageslower bandwidth-intensive operations. Such allocation of functions is anexample only. In some implementations, the high-speed interface 708 iscoupled to the memory 704, the display 716 (e.g., through a graphicsprocessor or accelerator), and to the high-speed expansion ports 710,which may accept various expansion cards (not shown). In theimplementation, the low-speed interface 712 is coupled to the storagedevice 706 and the low-speed expansion port 714. The low-speed expansionport 714, which may include various communication ports (e.g., USB,Bluetooth®, Ethernet, wireless Ethernet) may be coupled to one or moreinput/output devices, such as a keyboard, a pointing device, a scanner,or a networking device such as a switch or router, e.g., through anetwork adapter.

The computing device 700 may be implemented in a number of differentforms, as shown in the figure. For example, it may be implemented as astandard server 720, or multiple times in a group of such servers. Inaddition, it may be implemented in a personal computer such as a laptopcomputer 722. It may also be implemented as part of a rack server system724. Alternatively, components from the computing device 700 may becombined with other components in a mobile device (not shown), such as amobile computing device 750. Each of such devices may contain one ormore of the computing device 700 and the mobile computing device 750,and an entire system may be made up of multiple computing devicescommunicating with each other.

The mobile computing device 750 includes a processor 752, a memory 764,an input/output device such as a display 754, a communication interface766, and a transceiver 768, among other components. The mobile computingdevice 750 may also be provided with a storage device, such as amicro-drive or other device, to provide additional storage. Each of theprocessor 752, the memory 764, the display 754, the communicationinterface 766, and the transceiver 768, are interconnected using variousbuses, and several of the components may be mounted on a commonmotherboard or in other manners as appropriate.

The processor 752 can execute instructions within the mobile computingdevice 750, including instructions stored in the memory 764. Theprocessor 752 may be implemented as a chipset of chips that includeseparate and multiple analog and digital processors. The processor 752may provide, for example, for coordination of the other components ofthe mobile computing device 750, such as control of user interfaces,applications run by the mobile computing device 750, and wirelesscommunication by the mobile computing device 750.

The processor 752 may communicate with a user through a controlinterface 758 and a display interface 756 coupled to the display 754.The display 754 may be, for example, a TFT (Thin-Film-Transistor LiquidCrystal Display) display or an OLED (Organic Light Emitting Diode)display, or other appropriate display technology. The display interface756 may include appropriate circuitry for driving the display 754 topresent graphical and other information to a user. The control interface758 may receive commands from a user and convert them for submission tothe processor 752. In addition, an external interface 762 may providecommunication with the processor 752, so as to enable near areacommunication of the mobile computing device 750 with other devices. Theexternal interface 762 may provide, for example, for wired communicationin some implementations, or for wireless communication in otherimplementations, and multiple interfaces may also be used.

The memory 764 stores information within the mobile computing device750. The memory 764 can be implemented as one or more of acomputer-readable medium or media, a volatile memory unit or units, or anon-volatile memory unit or units. An expansion memory 774 may also beprovided and connected to the mobile computing device 750 through anexpansion interface 772, which may include, for example, a SIMM (SingleIn Line Memory Module) card interface. The expansion memory 774 mayprovide extra storage space for the mobile computing device 750, or mayalso store applications or other information for the mobile computingdevice 750. Specifically, the expansion memory 774 may includeinstructions to carry out or supplement the processes described above,and may include secure information also. Thus, for example, theexpansion memory 774 may be provide as a security module for the mobilecomputing device 750, and may be programmed with instructions thatpermit secure use of the mobile computing device 750. In addition,secure applications may be provided via the SIMM cards, along withadditional information, such as placing identifying information on theSIMM card in a non-hackable manner.

The memory may include, for example, flash memory and/or NVRAM memory(non-volatile random access memory), as discussed below. In someimplementations, instructions are stored in an information carrier. Theinstructions, when executed by one or more processing devices (forexample, processor 752), perform one or more methods, such as thosedescribed above. The instructions can also be stored by one or morestorage devices, such as one or more computer- or machine-readablemediums (for example, the memory 764, the expansion memory 774, ormemory on the processor 752). In some implementations, the instructionscan be received in a propagated signal, for example, over thetransceiver 768 or the external interface 762.

The mobile computing device 750 may communicate wirelessly through thecommunication interface 766, which may include digital signal processingcircuitry where necessary. The communication interface 766 may providefor communications under various modes or protocols, such as GSM voicecalls (Global System for Mobile communications), SMS (Short MessageService), EMS (Enhanced Messaging Service), or MMS messaging (MultimediaMessaging Service), CDMA (code division multiple access), TDMA (timedivision multiple access), PDC (Personal Digital Cellular), WCDMA(Wideband Code Division Multiple Access), CDMA2000, or GPRS (GeneralPacket Radio Service), among others. Such communication may occur, forexample, through the transceiver 768 using a radio-frequency. Inaddition, short-range communication may occur, such as using aBluetooth®, Wi-Fi™, or other such transceiver (not shown). In addition,a GPS (Global Positioning System) receiver module 770 may provideadditional navigation- and location-related wireless data to the mobilecomputing device 750, which may be used as appropriate by applicationsrunning on the mobile computing device 750.

The mobile computing device 750 may also communicate audibly using anaudio codec 760, which may receive spoken information from a user andconvert it to usable digital information. The audio codec 760 maylikewise generate audible sound for a user, such as through a speaker,e.g., in a handset of the mobile computing device 750. Such sound mayinclude sound from voice telephone calls, may include recorded sound(e.g., voice messages, music files, etc.) and may also include soundgenerated by applications operating on the mobile computing device 750.

The mobile computing device 750 may be implemented in a number ofdifferent forms, as shown in the figure. For example, it may beimplemented as a cellular telephone 780. It may also be implemented aspart of a smart-phone 782, personal digital assistant, or other similarmobile device.

Various implementations of the systems and techniques described here canbe realized in digital electronic circuitry, integrated circuitry,specially designed ASICs (application specific integrated circuits),computer hardware, firmware, software, and/or combinations thereof.These various implementations can include implementation in one or morecomputer programs that are executable and/or interpretable on aprogrammable system including at least one programmable processor, whichmay be special or general purpose, coupled to receive data andinstructions from, and to transmit data and instructions to, a storagesystem, at least one input device, and at least one output device.

These computer programs (also known as programs, software, softwareapplications or code) include machine instructions for a programmableprocessor, and can be implemented in a high-level procedural and/orobject-oriented programming language, and/or in assembly/machinelanguage. As used herein, the terms machine-readable medium andcomputer-readable medium refer to any computer program product,apparatus and/or device (e.g., magnetic discs, optical disks, memory,Programmable Logic Devices (PLDs)) used to provide machine instructionsand/or data to a programmable processor, including a machine-readablemedium that receives machine instructions as a machine-readable signal.The term machine-readable signal refers to any signal used to providemachine instructions and/or data to a programmable processor.

To provide for interaction with a user, the systems and techniquesdescribed here can be implemented on a computer having a display device(e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor)for displaying information to the user and a keyboard and a pointingdevice (e.g., a mouse or a trackball) by which the user can provideinput to the computer. Other kinds of devices can be used to provide forinteraction with a user as well; for example, feedback provided to theuser can be any form of sensory feedback (e.g., visual feedback,auditory feedback, or tactile feedback); and input from the user can bereceived in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in acomputing system that includes a back end component (e.g., as a dataserver), or that includes a middleware component (e.g., an applicationserver), or that includes a front end component (e.g., a client computerhaving a graphical user interface or a Web browser through which a usercan interact with an implementation of the systems and techniquesdescribed here), or any combination of such back end, middleware, orfront end components. The components of the system can be interconnectedby any form or medium of digital data communication (e.g., acommunication network). Examples of communication networks include alocal area network (LAN), a wide area network (WAN), and the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

In view of the structure, functions and apparatus of the systems andmethods described here, in some implementations, a system and method foridentification of polymer species from mass spectrometry output areprovided. Having described certain implementations of methods andapparatus for supporting identification of polymer species from massspectrometry output, it will now become apparent to one of skill in theart that other implementations incorporating the concepts of thedisclosure may be used. Therefore, the disclosure should not be limitedto certain implementations, but rather should be limited only by thespirit and scope of the following claims.

What is claimed is:
 1. A method for identifying a species of anunidentified chemical compound, the method comprising: accessing, by aprocessor of a computing device, mass spectrometry data for a samplecomprising the unidentified chemical compound; identifying, by theprocessor, at least a first candidate chemical formula for theunidentified chemical compound based at least in part on the massspectrometry data; determining, by the processor, the first candidatechemical formula is a neutral loss match to the unidentified chemicalcompound, said determining of the neutral loss match comprising, foreach of a plurality of spectral peaks of the mass spectrometry data:calculating a respective mass difference between a theoretical mass ofthe first candidate chemical formula and a respective experimental masscorresponding to the spectral peak, and comparing the respective massdifference with a mass of each of one or more corresponding neutralmolecular compositions to identify one or more candidate neutralmolecular compositions corresponding to the spectral peak.
 2. The methodof claim 1, wherein the mass spectrometry data comprises acollision-induced dissociation (CID) mass spectrum.
 3. The method ofclaim 1, comprising: identifying, by the processor, a second candidatechemical formula; determining, by the processor, the second candidatechemical formula is a neutral loss match to the unidentified chemicalcompound; and ranking, by the processor, the first candidate chemicalformula and the second candidate chemical formula as matches to theunknown chemical compound based in part upon similarity in neutral lossmatch.
 4. The method of claim 1, wherein identifying the one or morecandidate neutral molecular compositions comprises identifying that eachcandidate neutral molecular composition of the one or more candidateneutral molecular compositions having a respective mass within range ofa mass measurement accuracy of the respective experimental mass of thespectral peak.
 5. The method of claim 1, wherein determining the firstcandidate chemical formula is a neutral loss match to the unidentifiedchemical compound comprises identifying that a stoichiometry of thefirst candidate chemical formula allows for at least a first candidateneutral molecular composition of the one or more candidate neutralmolecular compositions.
 6. The method of claim 5, wherein identifyingthat the stoichiometry of the first candidate chemical formula allowsfor the first candidate neutral molecular composition comprisesdetermining, for the first candidate neutral molecular composition, anumber of atoms of each type in the first candidate chemical formula isgreater than a number of atoms of each corresponding type in thecandidate neutral loss composition.
 7. The method of claim 1, whereinthe unidentified chemical compound comprises a repeating structuralunit, and optionally one or more end units and identifying the firstcandidate chemical formula comprises: determining at least one of (a) achemical formula of the repeating structural unit, and (b) an estimatedmass of a portion of the unidentified chemical compound made up of therepeating structural unit; and identifying the first candidate chemicalformula for the unidentified chemical compound based at least in partupon the mass spectrometry data, and based further in part on at leastone of (a) the chemical formula of the repeating structural unit, and(b) the estimated mass of the portion of the unidentified chemicalcompound made up of the repeating structural unit.
 8. A systemcomprising: a processor; and a memory having instructions storedthereon, wherein the instructions, when executed by the processor, causethe processor to: access mass spectrometry data for a sample comprisingan unidentified chemical compound; identify at least a first candidatechemical formula for the unidentified chemical compound based at leastin part on the mass spectrometry data; and determine the first candidatechemical formula is a neutral loss match to the unidentified chemicalcompound, said determining of the neutral loss match comprising, foreach of a plurality of spectral peaks of the mass spectrometry data:calculating a respective mass difference between a theoretical mass ofthe first candidate chemical formula and a respective experimental masscorresponding to the spectral peak, and comparing the respective massdifference with a mass of each of one or more corresponding neutralmolecular compositions to identify one or more candidate neutralmolecular compositions corresponding to the spectral peak.
 9. Anon-transitory computer readable medium having instructions storedthereon, wherein the instructions, when executed by a processor, causethe processor to: access mass spectrometry data for a sample comprisingan unidentified chemical compound; identify at least a first candidatechemical formula for the unidentified chemical compound based at leastin part on the mass spectrometry data; and determine the first candidatechemical formula is a neutral loss match to the unidentified chemicalcompound, said determining of the neutral loss match comprising, foreach of a plurality of spectral peaks of the mass spectrometry data:calculating a respective mass difference between a theoretical mass ofthe first candidate chemical formula and a respective experimental masscorresponding to the spectral peak, and comparing the respective massdifference with a mass of each of one or more corresponding neutralmolecular compositions to identify one or more candidate neutralmolecular compositions corresponding to the spectral peak.
 10. Themethod of claim 1, comprising generating the mass spectrometry data forthe sample comprising the unidentified chemical compound.
 11. The methodof claim 1, wherein identifying at least a first candidate chemicalformula for the unidentified chemical compound comprises comparing amonoisotopic mass determined from the mass spectrometry data to amonoisotopic mass of each of one or more candidate parent ions.
 12. Themethod of claim 11 wherein the first candidate chemical formulacorresponds to a chemical formula of a parent ion having a monoisotopicmass within a predefined tolerance of the monoisotopic mass determinedfrom the mass spectrometry data.
 13. The method of claim 2, wherein eachof the plurality of spectral peaks of the mass spectrometry data is apeak in the CID mass spectrum.
 14. The method of claim 1, wherein thetheoretical mass of the first candidate chemical formula is a mass of amonoisotopic peak.
 15. The method of claim 7, wherein identifying thefirst candidate chemical formula comprises: determining a calculatedmass of the first candidate chemical formula based on at least one of(a) the chemical formula of the repeating structural unit, and (b) theestimated mass of the portion of the unidentified chemical compound madeup of the repeating structural unit; and determining the calculated massof the first candidate chemical formula is within a predefined thresholdof the estimated mass of the unidentified chemical compound.
 16. Themethod of claim 15, wherein the calculated mass of the first candidatechemical formula corresponds to a mass of a monoisotopic peak of thefirst candidate chemical formula, and the estimated mass of theunidentified chemical compound corresponds to a mass of the monoisotopicpeak of the unidentified chemical compound.
 17. The system of claim 8,comprising a mass spectrometer for generating the mass spectrometry datafor the sample comprising the unidentified chemical compound.
 18. Thesystem of claim 17, wherein the mass spectrometer is a tandem massspectrometer.
 19. The system of claim 8, wherein the instructions causethe processor to: identify a second candidate chemical formula;determine the second candidate chemical formula is a neutral loss matchto the unidentified chemical compound; and rank the first candidatechemical formula and the second candidate chemical formula as matches tothe unknown chemical compound based in part upon similarity in neutralloss match.
 20. The non-transitory computer readable medium of claim 9,wherein the instructions cause the processor to: identify a secondcandidate chemical formula; determine the second candidate chemicalformula is a neutral loss match to the unidentified chemical compound;and rank the first candidate chemical formula and the second candidatechemical formula as matches to the unknown chemical compound based inpart upon similarity in neutral loss match.